Analyzing and Summarizing Many Measurements

ByRoger Mead, Robert N. Curnow, Anne M. Hasted

This chapter introduces two of the most widely used techniques, principal component analysis and hierarchical cluster analysis. It argues that the small percentage of variability ignored can be attributed to random measurement variability unrelated to the flavor and texture differences between the apple varieties. The chapter illustrates the techniques with data from a sensory study in food science. In scientific studies where the objects or units are described by a multivariate set of measured variables, the measure of similarity or dissimilarity can be derived from the data in many different ways. In showing which varieties are similar in eating characteristics and which are different, the principal component solution is probably to be preferred since it gives some indication of why particular varieties cluster together. The cluster analysis approach will attempt to provide a solution regardless of the underlying dimensionality of the data.